Systems and methods of adjustable component management for a vehicle

The present disclosure generally relates to managing adjustable components for a vehicle and, more specifically to managing actuation of components of the vehicle based on occupancy of the vehicle.

Conventional monitoring techniques may involve complex image processing algorithms to detect depth. The need for dynamic depth detection is desired.

According to a first aspect of the present disclosure, a system for managing adjustments for a component of a vehicle includes a time-of-flight sensor configured to generate a point cloud representing a compartment of the vehicle. The point cloud includes three-dimensional positional information about the compartment. The system further includes at least one actuator configured to adjust the component of the vehicle. The system further includes processing circuitry in communication with the time-of-flight sensor and the at least one actuator, the processing circuitry configured to detect an occupant in a seat of the vehicle based on the point cloud, define a first portion of the point cloud corresponding to the occupant and a second portion of the point cloud corresponding to the seat of the vehicle, calculate a volume of the occupant based on the first portion of the point cloud, estimate a bodyweight of the occupant based on the volume, and communicate an instruction to adjust the component of the vehicle in response to the estimation of the bodyweight.

Embodiments of the first aspect of the present disclosure can include any one or a combination of the following features:

According to a second aspect of the present disclosure, a method for managing adjustments for a component of a vehicle includes generating, via a time-of-flight sensor, a point cloud representing a compartment of the vehicle. The point cloud includes three-dimensional positional information about the compartment. The method further includes detecting, via processing circuitry in communication with the time-of-flight sensor, an occupant in a seat of the vehicle based on the point cloud. The method further includes defining a first portion of the point cloud corresponding to the occupant and a second portion of the point cloud corresponding to the seat of the vehicle. The method further includes calculating, via the processing circuitry, a volume of the occupant based on the first portion of the point cloud. The method further includes estimating a bodyweight of the occupant based on the volume. The method further includes communicating, via the processing circuitry, an instruction to adjust the component of the vehicle via at least one actuator in response to the estimation of the bodyweight.

Embodiments of the second aspect of the present disclosure can include any one or a combination of the following features:

According to a third aspect of the present disclosure, a system for managing adjustments for a component of a vehicle includes a time-of-flight sensor configured to generate a point cloud representing a compartment of the vehicle. The point cloud includes three-dimensional positional information about the compartment. The system further includes at least one actuator configured to adjust the component of the vehicle. The system further includes processing circuitry in communication with the time-of-flight sensor and the at least one actuator. The system further includes a database in communication with the processing circuitry, the database including skeleton model data, wherein the processing circuitry is configured to define a skeleton model for the occupant based on the point cloud and the skeleton model data, detect an occupant in a seat of the vehicle based on the point cloud, define a first portion of the point cloud corresponding to the occupant and a second portion of the point cloud corresponding to the seat of the vehicle, calculate a volume of the occupant based on the first portion of the point cloud and the skeleton model data, estimate a bodyweight of the occupant based on the volume, and communicate an instruction to adjust the component of the vehicle in response to the estimation of the bodyweight.

These and other features, advantages, and objects of the present disclosure will be further understood and appreciated by those skilled in the art by reference to the following specification, claims, and appended drawings.

Reference will now be made in detail to the present preferred embodiments of the disclosure, examples of which are illustrated in the accompanying drawings. Wherever possible, the same reference numerals will be used throughout the drawings to refer to the same or like parts. In the drawings, the depicted structural elements may or may not be to scale and certain components may or may not be enlarged relative to the other components for purposes of emphasis and understanding.

For purposes of description herein, the terms “upper,” “lower,” “right,” “left,” “rear,” “front,” “vertical,” “horizontal,” and derivatives thereof shall relate to the concepts as oriented in. However, it is to be understood that the concepts may assume various alternative orientations, except where expressly specified to the contrary. It is also to be understood that the specific devices and processes illustrated in the attached drawings, and described in the following specification, are simply exemplary embodiments of the inventive concepts defined in the appended claims. Hence, specific dimensions and other physical characteristics relating to the embodiments disclosed herein are not to be considered as limiting, unless the claims expressly state otherwise.

The present illustrated embodiments reside primarily in combinations of method steps and apparatus components related to adjustable component management for a vehicle. Accordingly, the apparatus components and method steps have been represented, where appropriate, by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the disclosure with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein. Further, like numerals in the description and drawings represent like elements.

As used herein, the term “and/or,” when used in a list of two or more items, means that any one of the listed items can be employed by itself, or any combination of two or more of the listed items, can be employed. For example, if a composition is described as containing components A, B, and/or C, the composition can contain A alone; B alone; C alone; A and B in combination; A and C in combination; B and C in combination; or A, B, and C in combination.

As used herein, the term “about” means that amounts, sizes, formulations, parameters, and other quantities and characteristics are not and need not be exact, but may be approximate and/or larger or smaller, as desired, reflecting tolerances, conversion factors, rounding off, measurement error and the like, and other factors known to those of skill in the art. When the term “about” is used in describing a value or an end-point of a range, the disclosure should be understood to include the specific value or end-point referred to. Whether or not a numerical value or end-point of a range in the specification recites “about,” the numerical value or end-point of a range is intended to include two embodiments: one modified by “about,” and one not modified by “about.” It will be further understood that the end-points of each of the ranges are significant both in relation to the other end-point, and independently of the other end-point.

The terms “substantial,” “substantially,” and variations thereof as used herein are intended to note that a described feature is equal or approximately equal to a value or description. For example, a “substantially planar” surface is intended to denote a surface that is planar or approximately planar. Moreover, “substantially” is intended to denote that two values are equal or approximately equal. In some embodiments, “substantially” may denote values within about 10% of each other, such as within about 5% of each other, or within about 2% of each other.

As used herein the terms “the,” “a,” or “an,” mean “at least one,” and should not be limited to “only one” unless explicitly indicated to the contrary. Thus, for example, reference to “a component” includes embodiments having two or more such components unless the context clearly indicates otherwise.

Referring generally to, the present disclosure generally relates to a detection systemfor a vehiclethat utilizes three-dimensional image sensing to detect information about an environmentin or around the vehicle. The three-dimensional image sensing may be accomplished via one or more time-of-flight (ToF) sensorsthat are configured to map a three-dimensional space such as an interiorof the vehicleand/or a region exteriorto the vehicle. For example, the one or more time-of-flight sensorsmay include at least one light detection and ranging (LiDAR) moduleconfigured to output pulses of light, measure a time of flight for the pulses of light to return from the environmentto the at least one LiDAR module, and generate at least one point cloudof the environmentbased on the time-of-flight of the pulses of light. In this way, the LiDAR modulemay provide information regarding three-dimensional shapes of the environmentbeing scanned, including geometries, proportions, or other measurement information related to the environmentand/or occupantsfor the vehicle.

The LiDAR modulesof the present disclosure may operate conceptually similar to a still frame or video stream, but instead of producing a flat image with contrast and color, the LiDAR modulemay provide information regarding three-dimensional shapes of the environmentbeing scanned. Using time-of-flight, the LiDAR modulesare configured to measure the round-trip time taken for light to be transmitted, reflected from a surface, and received at a sensor near the transmission source. The light transmitted may be a laser pulse. The light may be sent and received millions of times per second at various angles to produce a matrix of the reflected light points. The result is a single measurement point for each transmission and reflection representing distance and a coordinate for each measurement point. When the LiDAR modulescans the entire “frame,” or field of view, it generates an output known as a point cloudthat is a 3D representation of the features scanned.

In some examples, the LiDAR modulesof the present disclosure may be configured to capture the at least one point cloudindependent of visible-light illumination of the environment. For example, the LiDAR modulesmay not require ambient light to achieve the spatial mapping techniques of the present disclosure. For example, the LiDAR modulemay emit and receive infrared (IR) or near-infrared (NIR) light, and therefore generate the at least one point clouddespite visible-light conditions. Further, as compared to Radio Detection and Ranging (RADAR), the depth-mapping achieved by the LiDAR modulesmay have greater accuracy due to the rate at which the LiDAR pulses may be emitted and received (e.g., the speed of light). Further, the three-dimensional mapping may be achieved without utilizing radio frequencies (RF), and therefore may limit RF certifications for operation. Accordingly, sensors incorporated for monitoring frequencies and magnitudes of RF fields may be omitted by providing the present LiDAR modules.

Referring now more particularly to, a plurality of the LiDAR modulesmay be configured to monitor a compartmentof the vehicle. In the example illustrated in, the LiDAR modulesare configured with a field of viewthat covers the rear space of the vehicle, as well as the region exteriorto the vehicle. In this example, the region exteriorto the vehicleis a space behind the vehicleadjacent to an entry or an exit to the vehicle. In, the plurality of LiDAR modulesare configured to monitor a front space of the vehicle, with the field of viewof one or more of the plurality of LiDAR modulescovering a passenger cabinof the vehicle. As will be described further herein, it is contemplated that the plurality of LiDAR modulesmay be in communication with one another to allow the at least one point cloudcaptured from each LiDAR moduleto be compared to one another to render a greater-accuracy representation of the environment. For example, and as depicted in, the occupantor another user may direct a mobile devicetoward the environmentto generate an additional point cloudfrom a viewing angle different than the field-of-viewsof the LiDAR modulesof the vehicle. For example, the mobile devicemay be a cellular phone having one of the LiDAR modules. In general, the time-of-flight sensorsdisclosed herein may capture point cloudsof various features of the environment, such as seats, occupants, and various other surfaces or items present in the interioror the region exteriorto the vehicle. As will further be discussed herein, the present systemmay be operable to identify these features based on the at least one point cloudand make determinations and/or calculations based on the identities, spatio-temporal positions of the features, and/or other related aspects of the features detected in the at least one point cloud.

Referring now to, representations of at least one point cloudgenerated from the LiDAR modulesin the interiorsof the vehiclesof, respectively, are presented to illustrate the three-dimensional mapping of the present system. For example, the depictions of the at least one point cloudmay be considered three-dimensional images constructed by the LiDAR modulesand/or processors in communication with the LiDAR modules. Although the depictions of the at least one point cloudsillustrated inmay differ in appearance, it is contemplated that such difference may be a result of averaging depths of the pointsof each point cloudto render a surface () as opposed to individual dots (). The underlying 3D data may be generated the same way in either case.

Still referring to, each point cloudincludes the three-dimensional data (e.g., a three-dimensional location relative to the LiDAR module) for the various features in the interior. For example, the at least one point cloudmay generate 3D mapping of the occupantsor cargoin the interior. The three-dimensional data may include the rectilinear coordinates, with XYZ coordinates, of various pointson surfaces or other light-reflective features relative to the LiDAR module. It is contemplated that the coordinates of each pointmay be virtually mapped to an origin point other than the LiDAR module, such as a center of mass of the vehicle, a center of volume of the compartmentbeing monitored, or any other feasible origin point. By obtaining the three-dimensional data of the various features in the interiorand, in some cases, the region exteriorto the vehicle, the detection systemmay provide for enhanced monitoring methods to be performed without complex imaging methods, such as those incorporating stereoscopic imagers or other three-dimensional monitoring devices that may require higher computational power or decreased efficiencies.

Referring now to, at least a portion of the present detection systemis exemplarily applied to a target surface, such as to the cargoor other surfaces in the environmentof the vehicle. The systemmay include processing circuitry, which will be further discussed in relation to the proceeding figures, in communication with one or more of the time-of-flight sensors. In the present example, the time-of-flight sensorsinclude the LiDAR moduleseach having a light source, or emitter, and a sensorconfigured to detect reflection of the light emitted by the light sourceoff of the target surface. A controllerof the LiDAR moduleis in communication with the light sourceand the sensorand is configured to monitor the time-of-flight of the light pulses emitted by the light sourceand returned to the sensor. The controlleris also in communication with a power supplyconfigured to provide electrical power to the controller, the light source, the sensor, and a motorthat is controlled by the controller. In the present example, the LiDAR moduleincorporates opticsthat are mechanically linked to the motorand are configured to guide the light pulses in a particular direction. For example, the opticsmay include lenses or mirrors that are configured to change an angle of emission for the light pulses and/or return the light pulses to the sensor. For instance, the motormay be configured to rotate a mirror to cause light emitted from the light sourceto reflect off of the mirror at different angles depending on the rotational position of the motor.

In some examples, the opticsmay include a first portion associated with the sourceand a second portion associated with the sensor. For example, a first lens, which may move in response to the motor, may be configured to guide (e.g., collimate, focus) the light emitted by the source, and a second lens, which may be driven by a different motor or a different connection to the motor, may be configured to guide the light reflected off the target surfaceand returned to the sensor. Accordingly, the general configuration of the LiDAR modulemay incorporate a single housing having different sets of optics or a plurality of housings with different optics. For example, the sourcemay be located in a first housing of the LiDAR module, the sensormay be located in a second housing separate from or spaced from the first housing. In this way, each of the LiDAR modulesmay refer to any emitter/receiver combination system that emits LiDAR pulses and receives the LiDAR pulses either at a common location in the vehicleor at different locations in the vehicle.

The light emitted and received by the present LiDAR modulesmay have a wavelength in the range of between approximately 780 nanometers (nm) and 1700 nm. In some examples, the wavelength of the LiDAR is preferably in the range of between 900 nm and 1650 nm. In other examples, the wavelength of the LiDAR is preferably between 1500 nm and 1650 nm. In some examples, the wavelength of the LiDAR is preferably at least 1550 nm. It is contemplated that the particular wavelength/frequency employed by the LiDAR modulesmay be based on an estimated distance range for capturing the depth information. For example, for shorter ranges (e.g., between 1 m and 5 m) the LiDAR may operate with a greater wavelength of light (e.g., greater than 1000 nm). The LiDAR modulesof the present disclosure may be configured to output light, in the form of a laser, at a wavelength of at least 1550 nm while the motorrotates the opticsto allow mapping an area. In some examples, the LiDAR modulesof the present disclosure are configured to emit light having a wavelength of at least 1650 nm. Due to the relatively short distances scanned by the present LiDAR modules(e.g., between one and five meters), such relatively low infrared (IR) or near-infrared (NIR) may be employed to achieve the three-dimensional spatial mapping via the at least one point cloudwith low power requirements. The present LiDAR modulesmay be either single point-and-reflect modules or may operate in a rotational mode, as described above. In rotational mode, the LiDAR modulemay measure up to 360 degrees based on the rate of rotation, which may be between 1 and 100 Hertz or may be at least 60 rotations per minute (RPM) in some examples.

In the example depicted in, the time-of-flight for a first pulse of lightemitted by the light sourceand returned to the sensormay be less than a second time-of-flight for a second light pulse emitted by the light sourcereturned to the sensor. For example, the first pulse of lightmay travel a shorter distance than the second pulse of lightdue to a difference in depth, height, or width of the corresponding reflection pointon the target surface. In this way, the LiDAR modulemay generate the at least one point cloudto be representative of the environment(e.g., the target surfacein the present example) in three dimensions.

The processing circuitryof the present disclosure may be provided to amalgamate the point cloudfrom each of a plurality of the LiDAR modulesand process the coordinates of the features to determine an identity of the features, as well as to perform other processing techniques that will be further described herein. The processing circuitrymay include a first processorlocal to the vehicleand a second processorremote from the vehicle. Further, the processing circuitrymay include the controllerof the LiDAR module. In some examples, the controllermay be configured to generate or determine the at least one point cloudand/or point cloud data, and the first processormay be configured to receive the at least one point cloudfrom each LiDAR moduleand compile each point cloudof a common scene, such as the environment, to generate a more expansive or more accurate point cloudof the environment.

The second processor, which may be a part of a remote serverand in communication with the first processor, via a network, may be configured to perform various modifications and/or mapping of the at least one point cloudto target three-dimensional image data for the environment. For example, the servermay include an artificial intelligence (AI) engineconfigured to train machine learning modelsbased on the point cloud data captured via the LiDAR modulesand/or historical data previously captured by the time-of-flight sensors. The second processormay be in communication with the AI engine, as well as in communication with a databaseconfigured to store the target point cloud data and/or three-dimensional image information. Accordingly, the servermay incorporate a memory storing instructions that, when executed by the processor, causes the processing circuitryto compare the at least one point cloudto point cloud data corresponding to target conditions of the interiorand/or the region exteriorto the vehicle. In this way, the detection systemmay employ the processing circuitryto perform advanced detection techniques and to communicate with subsystems of the vehicle, as will be described in the proceeding figures. In this way, the detection systemmay be employed in tandem or in conjunction with other operational parameters for the vehicle. For example, the detection systemmay be configured for communicating notifications to the occupantsof alert conditions, controlling the various operational parameters in response to actions detected in the interior, activating or deactivating various subsystems of the vehicle, or interacting with any vehicle systems to effectuate operational adjustments.

Referring now to, the detection systemmay incorporate or be in communication with various systems of the vehicle(e.g., vehicle systems). For example, the processing circuitrymay be configured to communicate with an imaging systemthat includes imaging devices, such as cameras (e.g., red-, green-, and blue-pixel (RGB) or IR cameras). The processing circuitrymay further be in communication with other vehicle systems, such as a door control system, a window control system, a seat control system, a climate control system, a user interface, mirrors, a lighting system, a restraint control system, a powertrain, a power management system, or any other vehicle systems. Communication with the various vehicle systems may allow the processing circuitryto transmit and receive signals or instructions to the various vehicle systems based on processing of the at least one point cloudcaptured by the time-of-flight sensors. For example, when the processing circuitryidentifies a number of occupantsin the vehiclebased on the at least one point cloud, the processing circuitrymay communicate an instruction to adjust the seat control systemand/or the climate control system. Such operations should always be implemented in accordance with the owner manual and safety guidelines. In another non-limiting example, the processing circuitrymay receive information or signals from the lighting systemand control operation of the time-of-flight sensorsbased on the information from the lighting system. Accordingly, the processing circuitrymay control, or communicate instructions to control, the time-of-flight sensorsbased on information from the vehicle systems and/or may communicate signals or instructions to the various vehicle systems based on information received from the time-of-flight sensors.

The window control systemmay include a window motorfor controlling a position of a window of the vehicle. Further, the window control systemmay include dimming circuitryfor controlling an opacity and/or level of light transmitted between the interiorof the vehicleand the region exteriorto the vehicle. One or more sunroof motorsmay be provided with the window control systemfor controlling closing and opening of a sunroof panel. It is contemplated that other devices may be included in the window control system, such as window locks, window breakage detection sensors, and other features related to operation of the windows of the vehicle. By providing communication between the window control systemand processing circuitryof the present disclosure, the window control systemmay be configured to adjust one or more of its features based on conditions determined or detected by the processing circuitrybased on the at least one point cloud. Similarly, the window control systemmay transmit one or more signals to the processing circuitry, and the processing circuitrymay control operation of the time-of-flight sensorsbased on the signals from the window control system.

The climate control systemmay include one or more heating and cooling devices, as well as vents configured to distribute heated or cooled air into the interiorof the vehicle. Although not specifically enumerated in, the climate control systemmay be configured to actuate a vent to selectively limit and allow heated air or cooled air to circulate in the interiorof the vehicle. Further, the climate control systemmay be configured to operate heating, ventilation, and air conditioning (HVAC) systems to recirculate air or to vent air to the region exteriorto the vehicle.

The seat control systemmay include various positioning actuators, inflatable bladders, seat warmers, and/or other ergonomic and/or comfort features for seatsin the vehicle. For example, the seat control systemmay include motors configured to actuate the seatforward, backward, side to side, or rotationally. Both a backrest of the seatand a lower portion of the seatmay be configured to be adjusted by the positioning actuators. Such operations should always be implemented in accordance with the owner manual and safety guidelines. The inflatable bladdersmay be provided within the seatto adjust a firmness or softness of the seat, and seat warmersmay be provided for warming cushions in the seatfor comfort of the occupants. In one non-limiting example, the processing circuitrymay compare the position of the seatsbased on seat sensors, such as position sensors, occupancy detection sensors, or other sensors configured to monitor the seats, to the point cloud data captured by the time-of-flight sensorsin order to verify or check an estimated seat position based on the point cloud data. Such operations should always be implemented in accordance with the owner manual and safety guidelines. In other examples, the processing circuitrymay communicate one or more signals to the seat control systembased on body pose data identified in the at least one point cloud. In yet further examples, the processing circuitrymay be configured to adjust an operational parameter of the time-of-flight sensors, such as a scanning direction, a frequency of the LiDAR module, or the like, based on the position of the seatsbeing monitored by the time-of-flight sensors.

The user interfacemay include a human-machine interface (HMI)and/or may include audio devices, such as microphones and/or speakers, mechanical actuators, such as knobs, buttons, switches, and/or a touchscreenincorporated with the HMI. The human-machine interfacemay be configured to present various digital objects representing buttons for selection by the user via, for example, the touchscreen. In general, the user interfacemay communicate with the processing circuitryto activate or deactivate the time-of-flight sensors, adjust operational parameters of the time-of-flight sensors, or control other aspects of the time-of-flight sensors. Similarly, the processing circuitrymay be configured to communicate instructions to the user interfaceto present information and/or other data related to the detection and/or processing of the at least one point cloudbased on the time-of-flight sensors. It is further contemplated that the mobile devicemay incorporate a user interfaceto present similar options to the user at the mobile device.

Still referring to, other vehicle systems include the mirrors, the lighting system, and the restraint control system. These other vehicle systems may also be adjusted based on the at least one point cloudgenerated by the time-of-flight sensorsand processed by the processing circuitry. Additionally, subcomponents of these systems (e.g., sensors, processors) may be configured to send instructions or data to the processing circuitryto cause the processing circuitryto operate the time-of-flight sensorsin an adjusted operation. For example, the processing circuitrymay be configured to deactivate the time-of-flight sensorsin response to the lighting systemdetecting adequate lighting to allow for visible light and/or IR occupant monitoring. In some examples, the processing circuitrymay communicate an instruction to adjust a position of the mirrorsbased on the at least one point cloud. For example, the at least one point cloudmay demonstrate an event, such as an orientation of a driver, a position of another vehicle in the region exteriorto the vehicle, or any other positional feature, and generate a signal to the mirrors(or associated positioning members) to move the mirrorsto align a view with the event.

Referring again to, the vehiclemay include the powertrainthat incorporates an ignition system, a steering system, a transmission system, a brake system, and/or any other system configured to drive the motion of the vehicle. In some examples, the at least one point cloudcaptured by the time-of-flight sensorsmay be processed by the processing circuitryto determine target steering angles, rates of motion or speed changes, or other vehicle operations for the powertrain, and communicate the target operations to the powertrainto allow for at least partially autonomous control over the motion of the vehicle. Such at least partially autonomous control may include fully autonomous operation or semiautonomous operation of the vehicle. For example, the processing circuitrymay communicate signals to adjust the brake system, the ignition system, the transmission system, or another system of the powertrainto stop the vehicleor move the vehicle.

The processing circuitrymay further include an occupant monitoring modulethat may communicate with any of the vehicle systems described above, as well as the time-of-flight sensorsof the present disclosure. The occupant monitoring modulemay be configured to store various algorithms for detecting aspects related to the occupants. For example, the algorithms may be executed to monitor the interiorof the vehicleto identify occupantsin the vehicle, a number of occupants, or other occupancy features of the interiorusing the point cloud data and/or video or image data captured by the imaging system. Similarly, various seat sensorsof the seat control system, heating or cooling sensors that detect manual manipulation of the vents for heating or cooling control for the climate control system, inputs to the window control system, or any other sensor of the vehicle systems previously described may be processed in the occupant monitoring moduleto detect positions of occupantsin the vehicle, conditions of occupantsin the vehicle, states of occupantsin the vehicle, or any other relevant occupancy features that will be described herein. The processing circuitrymay also include various classification algorithms for classifying objects detected in the interior, such as for the cargo, mobile devices, animals, and any other living or nonliving item in the interior. Accordingly, the processing circuitrymay be configured to identify an event in the interioror predict an event based on monitoring of the interiorby utilizing information from the other vehicle systems.

In general, the detection systemmay provide for spatial mapping of the environmentof the vehicle. For example, the LiDAR modulesmay detect the position, in three-dimensional space, of objects, items, or other features in the interioror the region exteriorto the vehicle. Such positions, therefore, include depth information of the scene captured by the LiDAR module. As compared to a two-dimensional image captured by a camera, the at least one point cloudgenerated by the time-of-flight sensorallows for more efficient determination of how far the features are from the LiDAR moduleand from one another. Thus, complex image analysis techniques involving pixel analysis, comparisons of RGB values, or other techniques to estimate depth may be omitted due to utilization of the ToF sensors. Further, while multiple imaging devices from different angles of a common scene (e.g., a stereoscopic imager) may allow for more accurate estimation of depth information than those produced by a single camera, complex data processing techniques may be required for multiple cameras to be employed to gather the depth information. Further, such multi-camera systems may require additional weight, packaging volume, or other inefficiencies relative to the time-of-flight sensorsof the present disclosure.

Accordingly, the detection systemmay be computationally-efficient and/or power-efficient relative to two-dimensional and three-dimensional cameras for determining positional information. Further, other time-of-flight sensing techniques, such as RADAR, while providing depth information, may present certification issues based on RF requirements and may be less accurate than the present LiDAR modules. Further, a number of cameras used for monitoring the environmentmay be reduced, various presence detectors (vehicle seat sensors) may be omitted, and other sensors configured to determine positional information about the environmentmay be omitted due to the precision of the LiDAR. Thus, a solution may be provided by the detection systemby reducing the number of sensors required to monitor various aspects of the environment.

Referring now to, the detection systemmay be a system for managing adjustments for a component of the vehicle. The detection systemmay include the time-of-flight sensorconfigured to generate the at least one point cloudrepresenting the compartmentof the vehicle. The at least one point cloudmay include three-dimensional positional information about the compartment. At least one actuatoris configured to adjust the component of the vehicle. The processing circuitryis in communication with the time-of-flight sensorand the at least one actuator. The processing circuitryis configured to detect the occupantin the seatof the vehiclebased on the at least one point cloud. The processing circuitryis further configured to define a first portionof the at least one point cloudcorresponding to the occupant, and a second portioncorresponding to the seatof the vehicle. The processing circuitryis further configured to calculate a volume V of the occupantbased on the first portionof the at least one point cloud. The processing circuitryis further configured to estimate a bodyweight of the occupantbased on the volume V. The processing circuitryis further configured to communicate an instruction to adjust the component of the vehiclein response to the estimation of the bodyweight.

According to some examples, the processing circuitryis further configured to calculate a product of the volume V of the occupantand a density estimate ρ. The estimation of the bodyweight is based on the product of the volume V of the occupantand the density estimate ρ. For example, the density estimate ρ may be an average density for a human body. In some examples, the density estimate ρ is between 900 and a 1000 kg/mper meters cubed. In a preferred example, the density estimate ρ is approximately 985 kg/m. By multiplying the density estimate ρ by the volume V of the occupant, the mass of the occupant, and therefore the bodyweight, may be determined by the processing circuitry.

According to some examples, the detection systemmay include databases,that are in communication with the processing circuitry. The databases,include skeleton model data. The processing circuitryis configured to define a skeleton modelfor the occupantbased on the at least one point cloudand based on the skeleton model data. For example, while the three-dimensional positional information may reveal the volume V of the occupant, the estimation of the volume V may be further refined by applying skeleton model data stored in the skeleton model database. For example, the processing circuitrymay further be configured to determine a pose of the occupantbased on the first portionof the at least one point cloudand the skeleton model. The skeleton modelmay include keypoints-corresponding to a central axisof body segments of the occupant. The processing circuitrymay be configured to compare the keypoints-to the first portionof the at least one point cloud, calculate a part vof the volume V for each body segment based on the comparison of the keypoints-to the first portion, and calculate a sum of the parts vof the volume V to determine the volume V of the occupant. For example, the processing circuitrymay identify a head, a torso, arms, legs, joints, or any subparts thereof, based on the first portionof the at least one point cloudand calculate the part vof the volume V corresponding to the particular body segment. Based on an estimation of the central axisfor each body segment, the processing circuitrymay estimate the part vof the volume V corresponding to the body segment and add up, or sum, the parts of the volume v to determine the volume for the occupant. In this way, the LiDAR modulesof the present disclosure may be employed to more accurately estimate the volume V, in combination with application of the skeleton modelfor the occupant.

According to some examples, the processing circuitryis further configured to compare the first portionof the at least one point cloudto the second portionof the at least one point cloudand estimate a coronal planefor the occupantbased on the comparison of the first portionto the second portion. The keypoints-may correspond to the coronal planeof the occupant, in some examples. The calculation of the volume V may be based further on the keypoints-of the coronal plane. For example, estimation of the coronal planemay be based on the processing circuitrybeing configured to detect a first depthof a frontof the occupantbased on the first portion, detect a second depthof a seating surfaceof the seatbased on the second portion, and calculate an average depthbased on the first and second depths,. For example, if a chestof the occupantis detected having a first depth, or vehicle-forward coordinate, and the seating surfaceof, for example, a backrest of the seat, has a depth in a vehicle-forward dimension, the two depths may be compared to one another to determine a difference. In this way, the frontof the occupantand the seating surfacemay each be determined, and a point halfway between the seating surfaceand the chestmay be estimated by the processing circuitry. The point halfway between the first and second depths,may correspond to the coronal planeof the occupantor another medial position corresponding to the central axisof one or more of the body segments, as previously described. Thus, in general, the depth information of the surroundings of the occupantcompared to the depth information of the occupantmay be utilized with the present detection systemto estimate the central axisof each body segment to allow for more accurate estimations of the volume V of the occupant.

According to some examples, the processing circuitryis further configured to define a third portionof the at least one point cloudcorresponding to a structural surfaceof the compartment. The processing circuitryis further configured to compare the first portionof the at least one point cloudto the third portionof the at least one point cloudand adjust an activation of at least one component based on a comparison of the first portionto the third portion. For example, the processing circuitrymay be configured to determine an alignment vectorbetween the first portionof the third portionof the at least one point cloud. The adjustment of the activation may be based further on the alignment vector. For example, a projected motion of the occupantin response to movement of the vehiclemay correspond to the alignment vector, and the processing circuitrymay adjust activation of the at least one component to be between the first portionand the third portionof the at least one point cloud. In some examples, the at least one adjustable component includes at least one restraint,configured to align with the alignment vectorupon deploymentof the at least one restraint,. For example, the projected motion of the occupantmay be impeded or interposed by the at least one restraint,in response to the vehiclemoving.

According to some examples, the adjustment of the activation includes adjusting a timing of the deploymentbased on the comparison of the first portionand the second portion. For example, the bodyweight of the occupantmay be utilized by the detection systemto adjust the deployment, including a inflation pressure, a deflation pressure, a delay, an advance, or any other parameter related to the deploymentof the at least one restraint,. For example, the bodyweight of the occupantmay influence a rate of motion, a moment of inertia, a proclivity to the potential motion for the alignment vector, or any other kinematic response to movement of the vehicle. Further, the position of the seatmay influence activation or deactivation of the at least one restraint,.

According to some examples, the at least one actuatoris configured to adjust the seat. Such operations should always be implemented in accordance with the owner manual and safety guidelines. For example, upon detection of a particular bodyweight for the occupant, the processing circuitrymay communicate an instruction to adjust the seatto a target position corresponding to a target posture or comfort level for the occupantcorresponding to the bodyweight estimated by the detection system. Such operations should always be implemented in accordance with the owner manual and safety guidelines. In some examples, adjustment to the seatand the at least one restraint,may be performed by the detection systemin parallel or in tandem with one another. For example, adjustment to the position of the occupantbased on the bodyweight may result in adjustment to the at least one restraint,based on movement of the occupantfollowing adjustment to the seat. Such operations should always be implemented in accordance with the owner manual and safety guidelines. Thus, the present LiDAR modulesmay be employed to gather the precise three-dimensional positional information to allow for an adjustment to other vehicle systems in communication with the processing circuitryto allow for effective activation of the restraint control systemor another vehicle system.

Referring now more particularly to, an exemplary demonstration of estimation of the average depthof the occupant, and in particular, the torsoof the occupantis generally illustrated. As previously described, the processing circuitrymay define the first portionof the at least one point cloudcorresponding to the occupant, the second portionof the at least one point cloudcorresponding to the seat, and the third portionof the at least one point cloudcorresponding to structural surfacesin the passenger cabin. For example, the structural surfacesmay include a steering wheel, a dashboard, a headliner, a windshield, a window, or any other component in the passenger cabinthat may be able to be contacted by the occupant. The second portionof the at least one point cloudmay be representative of the seating surfaceof the seat, such as a backrest surface, an upper part surface, a lower portion of the seat, or any other seating surface. By measuring the depth of the chestrelative to the depth of the seating surface, the central axisof the torso, as well as at least some of the other body segments of the occupant, may be estimated by the processing circuitry.

Referring now more particularly to, the processing circuitrymay be configured to estimate other dimensions or proportions of the body segments of the occupantbased on an amount of contact with a cushionof the seatcompared to an estimated length of the body segment. For example, a contact length may be compared to lengths of the cushion, and either or both of the contact length of the lengths of the cushionmay be compared to a length of the torsoor any other body segment. Based on the relationship of the various lengths compared to known historical data corresponding to the occupantor corresponding to occupantshaving similar builds, including body shape, bodyweight, height, or another dimensional parameter, the processing circuitrymay generate a more accurate estimate for the bodyweight of the occupant. Thus, in addition to estimation of central points for the occupant(e.g., the coronal plane, the keypoints-), the present at least one point cloudgenerated by the LiDAR modulesmay allow for greater precision in estimating bodyweight by using predefined proportions of the lengths of the various body segments compared to the amount or contact length for the body segment with the cushion. For example, if approximately 80% of a back of the occupantis typically engaging or abutting the seating surfaceof the backrest surface, the processing circuitrymay estimate the height of the torsobased on determining where the back is not contacting the seating surfaceaccording to the dimensional data. For example, a space regionmay be defined by the processing circuitrycorresponding to space between the occupant'sback and the cushionof the seat. Based on the contact length, the processing circuitrymay determine the length of the torsowhich may be utilized for estimation of the volume V of the torsoand thus the volume V of the occupant.

Still referring to, the average depthestimated between the first depthand the second depthmay correspond to the location of the coronal planefor the occupantand/or a contourfor the occupantthat corresponds to the overall pose of the occupantassociated with a frontal view of the occupant. For example, while a frontal area of the occupantfrom a front-facing camera may allow for estimations of the volume V of the occupantbased on predefined relationships using, for example, the seat position sensorof the seatto detect how far away the occupantis from the camera and compare the area of the frontof the occupantto the known depth of the seat, the present detection systemmay employ LiDAR to more accurately determine the depth of the occupantto determine the volume V of the occupant. For example, because the pointsof the at least one point cloudmay include rectilinear coordinate information, a direct comparison of the pointsassociated with the seating surfacemay be compared to the pointscorresponding to the position of the occupant. The occupant'sclothes, the occupant'sskin, face, or any other part of the occupant. As previously described, the estimate may be further refined by determining the keypoints-of the skeleton modelto therefore estimate the center or middle of each body segment. This estimation may include the pose of the occupant, which may include information pertaining to the contourof the occupant. Therefore, as demonstrated in, an average centerline passing through the occupantalong the head, torso, and one or both legsmay aid in generating the contourto provide a more accurate estimation of the volume V.

It is contemplated that the contourdescribed herein may correspond to the coronal planeof the occupantwhen the occupantis in a position other than an upright standing pose. Accordingly, the coronal planemay correspond to the central part of the occupantthroughout each body segment and intersect with the central axisfor each body segment. However, it is contemplated that the present detection systemmay incorporate estimation of any other biometric plane, such as the transverse plane or the sagittal plane, in order to provide an accurate estimation for the keypoints-of the skeleton model.

Referring now to, the skeleton modelemployed by the present detection systemmay be configured with estimations for various body segments, including the body parts shown and described in. The particular proportions, or spacing, between the keypoints-, may vary depending on the size or shape of the occupant. Accordingly, the processing circuitrymay be configured to select a correlating skeleton modelfor the occupantbased on the dimensional information generated or captured by the at least one point cloud.

Referring more particularly to, the at least one point cloudillustrated inhas the selected skeleton modeloverlaid over the first portionof the at least one point cloud. As shown, the keypoints-may generally align with the central axisfor the body segments previously shown with respect to. In some examples, the central axisfor each body segment as estimated based on the at least one point cloudmay be adjusted or modified in response to selection of the estimated skeleton modelfor the occupant. Thus, by providing the skeleton modelalong with the at least one point cloudgenerated by the LiDAR modules, the present detection systemmay provide for a more accurate estimation of body proportions and, accordingly, a better estimation of bodyweight, height, and/or other biometric features. For example, body segments between the keypoints-may have orientations that may be misaligned with the estimated central axisfor each of the body segments as demonstrated by the offset of the skeleton modelto the central axisof the right upper-arm of the occupant. Further, the body segments may have lengths between keypoints-that may differ from the lengths between jointsas estimated based on the central axisfor each body segment based on the at least one point cloudalone.